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3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

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Dynamically constructed network with error correction for accurate ventricle volume estimation.

Gongning Luo¹, Wei Wang¹, Clara Tam²

¹School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.

Medical Image Analysis

|July 5, 2020

Summary

This study introduces a novel deep learning method for accurate automated ventricle volume estimation (AVVE) from cardiac MRI. The approach corrects estimation errors, improving clinical applicability for cardiac disease diagnosis.

Keywords:

Dynamically constructed network Ejection fraction correlation Residual correction Ventricle volume estimation

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Area of Science:

Medical Imaging
Artificial Intelligence
Cardiology

Background:

Automated ventricle volume estimation (AVVE) using cardiac magnetic resonance (CMR) is crucial for diagnosing cardiac diseases.
Current AVVE methods lack error correction, leading to inaccurate ejection fraction (EF) assessment and limiting clinical use.

Purpose of the Study:

To develop an accurate AVVE method with integrated error correction for enhanced clinical applicability.
To address the limitations of existing AVVE techniques in CMR image analysis.

Main Methods:

Proposed a novel dynamically constructed deep learning framework evolving a single model into a bi-model network for direct EF correlation.
Introduced an error correction strategy using dynamically created residual nodes with stochastic configurations and EF correlation constraints.
Formulated an end-to-end joint optimization framework for accurate ventricle volume estimation and error correction.

Main Results:

The proposed method significantly outperforms state-of-the-art techniques on large-scale CMR datasets.
Demonstrated effective error correction for AVVE, a novel contribution to the field.
Achieved high accuracy in ventricle volume estimation and EF assessment.

Conclusions:

The developed method offers a promising solution for clinically applicable AVVE on CMR images.
This work represents the first approach to incorporate error correction in AVVE.
The methodology shows potential for extension to other medical index estimation tasks.